CLASP: Cross-modal Salient Anchor-based Semantic Propagation for Weakly-supervised Dense Audio-Visual Event Localization
This addresses the challenge of localizing events in untrimmed videos with weak supervision, which is incremental as it builds on existing DAVEL tasks by introducing a more practical but constrained setting.
The paper tackles the problem of weakly-supervised dense audio-visual event localization (W-DAVEL), where only video-level labels are available, by exploiting cross-modal salient anchors to identify reliable timestamps with consistent event semantics across audio and visual modalities. The method achieves state-of-the-art performance on the UnAV-100 and ActivityNet1.3 datasets.
The Dense Audio-Visual Event Localization (DAVEL) task aims to temporally localize events in untrimmed videos that occur simultaneously in both the audio and visual modalities. This paper explores DAVEL under a new and more challenging weakly-supervised setting (W-DAVEL task), where only video-level event labels are provided and the temporal boundaries of each event are unknown. We address W-DAVEL by exploiting \textit{cross-modal salient anchors}, which are defined as reliable timestamps that are well predicted under weak supervision and exhibit highly consistent event semantics across audio and visual modalities. Specifically, we propose a \textit{Mutual Event Agreement Evaluation} module, which generates an agreement score by measuring the discrepancy between the predicted audio and visual event classes. Then, the agreement score is utilized in a \textit{Cross-modal Salient Anchor Identification} module, which identifies the audio and visual anchor features through global-video and local temporal window identification mechanisms. The anchor features after multimodal integration are fed into an \textit{Anchor-based Temporal Propagation} module to enhance event semantic encoding in the original temporal audio and visual features, facilitating better temporal localization under weak supervision. We establish benchmarks for W-DAVEL on both the UnAV-100 and ActivityNet1.3 datasets. Extensive experiments demonstrate that our method achieves state-of-the-art performance.